相关实验视频
Updated: Jan 20, 2026
08:34
Robot-assisted Partial Splenectomy
Published on: January 2, 2026
362
问或不问:人类在循环中的上下文强盗与机器人辅助养中的应用
Rohan Banerjee1, Rajat Kumar Jenamani1, Sidharth Vasudev1
1Department of Computer Science, Cornell University.
IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation
|January 19, 2026
概括
这项研究引入了机器人辅助进食的人在循环系统,可以选择性地查询用户,平衡任务效率和减少用户工作负载. 该方法适应个人需求,特别是对于那些移动性有限的人来说.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
- 人工智能的人工智能
背景情况:
- 机器人辅助的咬伤获取面临着各种食品的挑战.
- 完全自主系统很难在各种食物属性上进行概括.
- 用户反至关重要,但可以增加工作量.
研究的目的:
- 为机器人辅助咬咬获取开发一个人-在-循环策略.
- 为了尽量减少用户查询工作负载,同时保持任务性能.
- 根据用户的工作量和限制,调整查询行为.
主要方法:
- 在上下文的盗框架内制定了人类在循环中的咬伤获取.
- 介绍了LINUCB-QG,一种使用查询工作负载预测模型的方法.
- 训练模型的数据来自在线研究与不同的参与者群体.
主要成果:
- 提出的方法有效地平衡了任务性能和查询工作负载.
- LINUCB-QG的表现优于自主和始终查询的基线.
- 查询行为调整以适应移动性有限的用户的较高工作负载.
结论:
- 利用选择性用户反改进了机器人辅助进食系统.
- 该LINUCB-QG方法提供了一个适应性和高效的方法.
- 这项研究对辅助机器人和个性化护理有影响.
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